Systems and Methods for Early Detection of Network Fraud Events

2. The computing system of claim 1, wherein the at least one processor is further configured to set the common starting point to be one of a present time and a timestamp associated with the most recently received one of the scored transaction authorization requests.

4. The computing system of claim 3, wherein the downstream fraud detection model is a machine learning model, and wherein the at least one processor is further configured to use the ratios to calculate feature inputs to the machine learning model.

5. The computing system of claim 1, wherein the at least one processor is further configured to determine at least one of the plurality of account ranges based on a corresponding bank identification number (BIN).

6. The computing system of claim 1, wherein the at least one processor is further configured to output a potential fraud attack alert associated with all account numbers within the one of the account ranges.

7. The computing system of claim 1, wherein the at least one processor is further configured to calculate the cumulative metric within each of the shorter and longer time periods using at least one of i) a tally of the scored transaction authorization requests within the respective fraud score range stripe and the respective account range, and ii) a cumulative total of transaction amounts of the scored transaction authorization requests within the respective fraud score range stripe and the respective account range.

9. The computer-implemented method of claim 8, further comprising setting, by the at least one processor, the common starting point to be one of a present time and a timestamp associated with the most recently received one of the scored transaction authorization requests.

11. The computer-implemented method of claim 10, wherein the downstream fraud detection model is a machine learning model, said method further comprising using, by the at least one processor, the ratios to calculate feature inputs to the machine learning model.

12. The computer-implemented method of claim 8, further comprising determining, by the at least one processor, at least one of the plurality of account ranges based on a corresponding bank identification number (BIN).

13. The computer-implemented method of claim 8, further comprising outputting, by the at least one processor, a potential fraud attack alert associated with all account numbers within the one of the account ranges.

14. The computer-implemented method of claim 8, further comprising calculating, by the at least one processor, the cumulative metric within each of the shorter and longer time periods using at least one of i) a tally of the scored transaction authorization requests within the respective fraud score range stripe and the respective account range, and ii) a cumulative total of transaction amounts of the scored transaction authorization requests within the respective fraud score range stripe and the respective account range.

16. The at least one non-transitory computer-readable storage media of claim 15, wherein the computer-executable instructions further cause the at least one processor to set the common starting point to be one of a present time and a timestamp associated with the most recently received one of the scored transaction authorization requests.

18. The at least one non-transitory computer-readable storage media of claim 17, wherein the downstream fraud detection model is a machine learning model, and wherein the computer-executable instructions further cause the at least one processor to use the ratios to calculate feature inputs to the machine learning model.

19. The computing system of claim 1, wherein the computer-executable instructions further cause the at least one processor to determine at least one of the plurality of account ranges based on a corresponding bank identification number (BIN).

20. The computing system of claim 1, wherein the computer-executable instructions further cause the at least one processor to output a potential fraud attack alert associated with all account numbers within the one of the account ranges.